MATLAB and Simulink Training

Courses Listed by Learning Path

Getting Started

Start with a fundamental course, MATLAB Fundamentals or Simulink for System and Algorithm Modeling, and then build upon this core knowledge by following a recommended learning path.


MATLAB Learning Paths

Analysis and Visualization Path

Prerequisite

MATLAB Fundamentals

This course provides a comprehensive introduction to the MATLAB technical computing environment. No prior programming experience or knowledge of MATLAB is assumed. Themes of data analysis, visualization, modeling, and programming are explored throughout the course.

Core

MATLAB for Data Processing and Visualization

This course focuses on importing and preparing data for data analytics applications. The course is intended for data analysts and data scientists who need to automate the processing, analysis, and visualization of data from multiple sources.

Optional

Accelerating and Parallelizing MATLAB Code

This course covers a variety of techniques for making your MATLAB® code run faster. You will identify and remove computational bottle-necks using techniques like preallocation and vectorization. In addition, you will compile MATLAB code into MEX-files using MATLAB Coder™. On top of that, you will take advantage of multiple cores on your computer by parallelizing for-loops with Parallel Computing Toolbox™, and scale up across multiple computers using MATLAB Distributed Computing Server™. Interplay between those concepts will be explored throughout the course. If you are working with long-running simulations, you will benefit from the hands-on demonstrations and exercises in the course.

Statistics and Machine Learning Path

Prerequisite

MATLAB Fundamentals

This course provides a comprehensive introduction to the MATLAB technical computing environment. No prior programming experience or knowledge of MATLAB is assumed. Themes of data analysis, visualization, modeling, and programming are explored throughout the course.

Core

Statistical Methods in MATLAB

This course provides hands-on experience with performing statistical data analysis with MATLAB and Statistics and Machine Learning Toolbox. Examples and exercises demonstrate the use of appropriate MATLAB and Statistics and Machine Learning Toolbox functionality throughout the analysis process, from importing and organizing data, to exploratory analysis, to confirmatory analysis and simulation.


Machine Learning with MATLAB

This course focuses on data analytics and machine learning techniques in MATLAB® using functionality within Statistics and Machine Learning Toolbox™ and Neural Network Toolbox™. The course demonstrates the use of unsupervised learning to discover features in large data sets and supervised learning to build predictive models. Examples and exercises highlight techniques for visualization and evaluation of results.

Optional

Accelerating and Parallelizing MATLAB Code

This course covers a variety of techniques for making your MATLAB® code run faster. You will identify and remove computational bottle-necks using techniques like preallocation and vectorization. In addition, you will compile MATLAB code into MEX-files using MATLAB Coder™. On top of that, you will take advantage of multiple cores on your computer by parallelizing for-loops with Parallel Computing Toolbox™, and scale up across multiple computers using MATLAB Distributed Computing Server™. Interplay between those concepts will be explored throughout the course. If you are working with long-running simulations, you will benefit from the hands-on demonstrations and exercises in the course.

Code Development and Management Path

Prerequisite

MATLAB Fundamentals

This course provides a comprehensive introduction to the MATLAB technical computing environment. No prior programming experience or knowledge of MATLAB is assumed. Themes of data analysis, visualization, modeling, and programming are explored throughout the course.

Core

MATLAB Programming Techniques

This course provides hands-on experience using the features in the MATLAB language to write efficient, robust, and well-organized code. These concepts form the foundation for writing full applications, developing algorithms, and extending built-in MATLAB capabilities. Details of performance optimization, as well as tools for writing, debugging, and profiling code are covered.

Accelerating and Parallelizing MATLAB Code

This course covers a variety of techniques for making your MATLAB® code run faster. You will identify and remove computational bottle-necks using techniques like preallocation and vectorization. In addition, you will compile MATLAB code into MEX-files using MATLAB Coder™. On top of that, you will take advantage of multiple cores on your computer by parallelizing for-loops with Parallel Computing Toolbox™, and scale up across multiple computers using MATLAB Distributed Computing Server™. Interplay between those concepts will be explored throughout the course. If you are working with long-running simulations, you will benefit from the hands-on demonstrations and exercises in the course.

Optional

Object-Oriented Programming with MATLAB

Attendees will learn to use object-oriented programming techniques to develop and maintain complex MATLAB applications. In addition, ideas from a test-driven development approach to foster software quality and agility are introduced.

Interface Creation Path

Prerequisite

MATLAB Fundamentals

This course provides a comprehensive introduction to the MATLAB technical computing environment. No prior programming experience or knowledge of MATLAB is assumed. Themes of data analysis, visualization, modeling, and programming are explored throughout the course.

Core

Building Interactive Applications in MATLAB

This course demonstrates how to create an interactive user interface for your applications in MATLAB. Attendees will learn about user interface controls, such as push buttons and text boxes, and how to use them to create a robust and user-friendly interface to your MATLAB application. No prior experience of programming graphical interfaces is required.

MATLAB Programming Techniques

This course provides hands-on experience using the features in the MATLAB language to write efficient, robust, and well-organized code. These concepts form the foundation for writing full applications, developing algorithms, and extending built-in MATLAB capabilities. Details of performance optimization, as well as tools for writing, debugging, and profiling code are covered.

Time-Series Modeling Path

Prerequisite

MATLAB for Financial Applications

This course provides a comprehensive introduction to the MATLAB technical computing environment for financial professionals. The course is intended for beginning users and those looking for a review. No prior programming experience or knowledge of MATLAB is assumed. Themes of data analysis, visualization, modeling, and programming are explored throughout the course, with an emphasis on practical application to finance, such as time-series analysis, Monte Carlo simulation, portfolio management, and analysis.

Core

Time-Series Modeling in MATLAB

This course provides a comprehensive introduction to time-series modeling using MATLAB and Econometrics Toolbox. The course is intended for economists, analysts and other financial professionals with prior experience of MATLAB who require to develop and maintain time-series models. The course is designed to follow the standard Box-Jenkins procedure for developing time-series models.

Optional

Statistical Methods in MATLAB

This course provides hands-on experience with performing statistical data analysis with MATLAB and Statistics and Machine Learning Toolbox. Examples and exercises demonstrate the use of appropriate MATLAB and Statistics and Machine Learning Toolbox functionality throughout the analysis process, from importing and organizing data, to exploratory analysis, to confirmatory analysis and simulation.

Optimization Techniques in MATLAB

This course introduces applied optimization in the MATLAB environment, focusing on using Optimization Toolbox and Global Optimization Toolbox.

Risk Management Path

Prerequisite

MATLAB for Financial Applications

This course provides a comprehensive introduction to the MATLAB technical computing environment for financial professionals. The course is intended for beginning users and those looking for a review. No prior programming experience or knowledge of MATLAB is assumed. Themes of data analysis, visualization, modeling, and programming are explored throughout the course, with an emphasis on practical application to finance, such as time-series analysis, Monte Carlo simulation, portfolio management, and analysis.

Core

Risk Management with MATLAB

This course provides a comprehensive introduction to risk management using MATLAB and Financial Toolbox. The course is intended for risk analysts, risk managers, portfolio managers, and other financial professionals with prior experience of MATLAB who require to analyze, assess, and manage risk. The course uses examples from market and credit risk, although the techniques demonstrated are applicable in all risk areas, including interest-rate, liquidity, and operational risk.

Statistical Methods in MATLAB

This course provides hands-on experience with performing statistical data analysis with MATLAB and Statistics and Machine Learning Toolbox. Examples and exercises demonstrate the use of appropriate MATLAB and Statistics and Machine Learning Toolbox functionality throughout the analysis process, from importing and organizing data, to exploratory analysis, to confirmatory analysis and simulation.

Optional

Machine Learning with MATLAB

This course focuses on data analytics and machine learning techniques in MATLAB® using functionality within Statistics and Machine Learning Toolbox™ and Neural Network Toolbox™. The course demonstrates the use of unsupervised learning to discover features in large data sets and supervised learning to build predictive models. Examples and exercises highlight techniques for visualization and evaluation of results.

Accelerating and Parallelizing MATLAB Code

This course covers a variety of techniques for making your MATLAB® code run faster. You will identify and remove computational bottle-necks using techniques like preallocation and vectorization. In addition, you will compile MATLAB code into MEX-files using MATLAB Coder™. On top of that, you will take advantage of multiple cores on your computer by parallelizing for-loops with Parallel Computing Toolbox™, and scale up across multiple computers using MATLAB Distributed Computing Server™. Interplay between those concepts will be explored throughout the course. If you are working with long-running simulations, you will benefit from the hands-on demonstrations and exercises in the course.

Quantitative Finance Path

Prerequisite

MATLAB for Financial Applications

This course provides a comprehensive introduction to the MATLAB technical computing environment for financial professionals. The course is intended for beginning users and those looking for a review. No prior programming experience or knowledge of MATLAB is assumed. Themes of data analysis, visualization, modeling, and programming are explored throughout the course, with an emphasis on practical application to finance, such as time-series analysis, Monte Carlo simulation, portfolio management, and analysis.

Core

Statistical Methods in MATLAB

This course provides hands-on experience with performing statistical data analysis with MATLAB and Statistics and Machine Learning Toolbox. Examples and exercises demonstrate the use of appropriate MATLAB and Statistics and Machine Learning Toolbox functionality throughout the analysis process, from importing and organizing data, to exploratory analysis, to confirmatory analysis and simulation.

MATLAB Programming Techniques

This course provides hands-on experience using the features in the MATLAB language to write efficient, robust, and well-organized code. These concepts form the foundation for writing full applications, developing algorithms, and extending built-in MATLAB capabilities. Details of performance optimization, as well as tools for writing, debugging, and profiling code are covered.

Optional

Object-Oriented Programming with MATLAB

Attendees will learn to use object-oriented programming techniques to develop and maintain complex MATLAB applications. In addition, ideas from a test-driven development approach to foster software quality and agility are introduced.

Financial Application Development Path

Prerequisite

MATLAB for Financial Applications

This course provides a comprehensive introduction to the MATLAB technical computing environment for financial professionals. The course is intended for beginning users and those looking for a review. No prior programming experience or knowledge of MATLAB is assumed. Themes of data analysis, visualization, modeling, and programming are explored throughout the course, with an emphasis on practical application to finance, such as time-series analysis, Monte Carlo simulation, portfolio management, and analysis.

Core

MATLAB Programming Techniques

This course provides hands-on experience using the features in the MATLAB language to write efficient, robust, and well-organized code. These concepts form the foundation for writing full applications, developing algorithms, and extending built-in MATLAB capabilities. Details of performance optimization, as well as tools for writing, debugging, and profiling code are covered.

Building Interactive Applications in MATLAB

This course demonstrates how to create an interactive user interface for your applications in MATLAB. Attendees will learn about user interface controls, such as push buttons and text boxes, and how to use them to create a robust and user-friendly interface to your MATLAB application. No prior experience of programming graphical interfaces is required.

Optional

Object-Oriented Programming with MATLAB

Attendees will learn to use object-oriented programming techniques to develop and maintain complex MATLAB applications. In addition, ideas from a test-driven development approach to foster software quality and agility are introduced.

Accelerating and Parallelizing MATLAB Code

This course covers a variety of techniques for making your MATLAB® code run faster. You will identify and remove computational bottle-necks using techniques like preallocation and vectorization. In addition, you will compile MATLAB code into MEX-files using MATLAB Coder™. On top of that, you will take advantage of multiple cores on your computer by parallelizing for-loops with Parallel Computing Toolbox™, and scale up across multiple computers using MATLAB Distributed Computing Server™. Interplay between those concepts will be explored throughout the course. If you are working with long-running simulations, you will benefit from the hands-on demonstrations and exercises in the course.

Prerequisite

MATLAB Fundamentals

This course provides a comprehensive introduction to the MATLAB technical computing environment. No prior programming experience or knowledge of MATLAB is assumed. Themes of data analysis, visualization, modeling, and programming are explored throughout the course.

Core

Signal Processing with MATLAB

This course shows how to analyze signals and design signal processing systems using MATLAB, Signal Processing Toolbox, and DSP System Toolbox.

Optional

MATLAB to C with MATLAB Coder

This course focuses on generating C code from MATLAB code using MATLAB Coder. The focus is on developing MATLAB code that is ready for code generation, generating C code that meets optimization requirements, and integrating generated code into parent projects and external modules. This course is intended for intermediate to advanced MATLAB users.

Prerequisite

MATLAB Fundamentals

This course provides a comprehensive introduction to the MATLAB technical computing environment. No prior programming experience or knowledge of MATLAB is assumed. Themes of data analysis, visualization, modeling, and programming are explored throughout the course.

Core

Image Processing with MATLAB

This course provides hands-on experience with performing image analysis. Examples and exercises demonstrate the use of appropriate MATLAB and Image Processing Toolbox functionality throughout the analysis process.

Computer Vision with MATLAB

This course provides hands-on experience with performing computer vision tasks. Examples and exercises demonstrate the use of appropriate MATLAB and Computer Vision System Toolbox functionality.

Prerequisite

MATLAB Fundamentals

This course provides a comprehensive introduction to the MATLAB technical computing environment. No prior programming experience or knowledge of MATLAB is assumed. Themes of data analysis, visualization, modeling, and programming are explored throughout the course.

Core

Communication Systems Design with MATLAB

This course shows how to design and simulate digital communication systems using MATLAB. Different channel impairments and their modeling are demonstrated.

Designing LTE and LTE Advanced Physical Layer Systems with MATLAB

This course provides an overview of the LTE and LTE Advanced physical layer. Using MATLAB, and LTE System Toolbox, attendees will learn how to generate reference LTE waveforms and build and simulate an end-to-end LTE PHY model.

MathWorks Certified MATLAB Associate Path

Prerequisite

MATLAB Fundamentals

This course provides a comprehensive introduction to the MATLAB technical computing environment. No prior programming experience or knowledge of MATLAB is assumed. Themes of data analysis, visualization, modeling, and programming are explored throughout the course.

Core

MathWorks Certified MATLAB Associate Exam

Becoming a Certified MATLAB Associate is the first step in the MATLAB® certification track. Earning this credential validates your proficiency with MATLAB and can help you to enhance your credibility and accelerate your career. Mastery at this level also prepares you for the next challenging level of certification, Certified MATLAB Professional.

MathWorks Certified MATLAB Professional Path

Prerequisite

MathWorks Certified MATLAB Associate Exam

Becoming a Certified MATLAB Associate is the first step in the MATLAB® certification track. Earning this credential validates your proficiency with MATLAB and can help you to enhance your credibility and accelerate your career. Mastery at this level also prepares you for the next challenging level of certification, Certified MATLAB Professional.

MATLAB Programming Techniques

This course provides hands-on experience using the features in the MATLAB language to write efficient, robust, and well-organized code. These concepts form the foundation for writing full applications, developing algorithms, and extending built-in MATLAB capabilities. Details of performance optimization, as well as tools for writing, debugging, and profiling code are covered.

MATLAB for Data Processing and Visualization

This course focuses on importing and preparing data for data analytics applications. The course is intended for data analysts and data scientists who need to automate the processing, analysis, and visualization of data from multiple sources.

Building Interactive Applications in MATLAB

This course demonstrates how to create an interactive user interface for your applications in MATLAB. Attendees will learn about user interface controls, such as push buttons and text boxes, and how to use them to create a robust and user-friendly interface to your MATLAB application. No prior experience of programming graphical interfaces is required.

Core

MathWorks Certified MATLAB Professional Exam

Once you’ve obtained your Certified MATLAB Associate credential, you are ready to build on your certification portfolio and master the next level, Certified MATLAB Professional. Earning the Certified MATLAB Professional credential demonstrates that you have expanded your basic MATLAB skills to a level of mastery on par with the proficiency of the most advanced members of the MATLAB community.


Simulink Learning Paths

Supervisory Control Logic Path

Prerequisite

Simulink for System and Algorithm Modeling

This course is for engineers who are new to system and algorithm modeling and design validation in Simulink. It demonstrates how to apply basic modeling techniques and tools to develop Simulink block diagrams.

Core

Stateflow for Logic Driven System Modeling

This course shows how to implement complex decision flows and finite-state machines using Stateflow. The course focuses on how to employ flow graphs, state machines, and truth tables in Simulink designs.

Embedded Coder for Production Code Generation

This hands-on course focuses on developing models in the Simulink environment to deploy on embedded systems. The course is designed for Simulink users who intend to generate, validate, and deploy embedded code using Embedded Coder.

Control Design and Analysis Path

Prerequisite

Simulink for System and Algorithm Modeling

This course is for engineers who are new to system and algorithm modeling and design validation in Simulink. It demonstrates how to apply basic modeling techniques and tools to develop Simulink block diagrams.

Core

Control System Design with MATLAB and Simulink

This course provides a general understanding of how to accelerate the design process for closed-loop control systems using MATLAB and Simulink.

Embedded Coder for Production Code Generation

This hands-on course focuses on developing models in the Simulink environment to deploy on embedded systems. The course is designed for Simulink users who intend to generate, validate, and deploy embedded code using Embedded Coder.

Embedded Systems Path

Prerequisite

Simulink for System and Algorithm Modeling

This course is for engineers who are new to system and algorithm modeling and design validation in Simulink. It demonstrates how to apply basic modeling techniques and tools to develop Simulink block diagrams.

Core

Testing Generated Code in Simulink

This course provides a working introduction to designing and testing embedded applications with Simulink Coder and Embedded Coder. Themes of simulation speedup, parameter tuning in the deployed application, structure of embedded code, code verification, and execution profiling are explored in the context of Model-Based Design.

Embedded Coder for Production Code Generation

This hands-on course focuses on developing models in the Simulink environment to deploy on embedded systems. The course is designed for Simulink users who intend to generate, validate, and deploy embedded code using Embedded Coder.

Optional

Programming Xilinx Zynq SoCs with MATLAB and Simulink

This hands-on course focuses on developing and configuring models in the Simulink environment and deploying on Xilinx Zynq-7000 All Programmable SoCs. The course is designed for Simulink users who intend to generate, validate, and deploy embedded code and HDL code for software/hardware codesign using Embedded Coder and HDL Coder. A ZedBoard is provided to each attendee for use throughout the course. The board is programmed during the class and is yours to keep after the training.

Polyspace for C/C++ Code Verification

This course discusses the use of Polyspace Code Prover to prove code correctness, improve software quality metrics, and ensure product integrity. This hands-on course is intended for engineers who develop software or models targeting embedded systems.

FPGA Design Path

Prerequisite

Simulink for System and Algorithm Modeling

This course is for engineers who are new to system and algorithm modeling and design validation in Simulink. It demonstrates how to apply basic modeling techniques and tools to develop Simulink block diagrams.

Core

Testing Generated Code in Simulink

This course provides a working introduction to designing and testing embedded applications with Simulink Coder and Embedded Coder. Themes of simulation speedup, parameter tuning in the deployed application, structure of embedded code, code verification, and execution profiling are explored in the context of Model-Based Design.

Generating HDL Code from Simulink

This course shows how to generate and verify HDL code from a Simulink model using HDL Coder and HDL Verifier.

Optional

Programming Xilinx Zynq SoCs with MATLAB and Simulink

This hands-on course focuses on developing and configuring models in the Simulink environment and deploying on Xilinx Zynq-7000 All Programmable SoCs. The course is designed for Simulink users who intend to generate, validate, and deploy embedded code and HDL code for software/hardware codesign using Embedded Coder and HDL Coder. A ZedBoard is provided to each attendee for use throughout the course. The board is programmed during the class and is yours to keep after the training.

Prerequisite

Simulink for System and Algorithm Modeling

This course is for engineers who are new to system and algorithm modeling and design validation in Simulink. It demonstrates how to apply basic modeling techniques and tools to develop Simulink block diagrams.

Core

Simulink Model Management and Architecture

This course describes techniques for applying Model-Based Design in a common design workflow. It provides guidance on managing and sharing Simulink models when working in a large-scale project environment.

Verification and Validation of Simulink Models

This course describes techniques for testing and formally verifying Simulink model behavior.

Mechanical System Modeling Path

Prerequisite

Simulink for System and Algorithm Modeling

This course is for engineers who are new to system and algorithm modeling and design validation in Simulink. It demonstrates how to apply basic modeling techniques and tools to develop Simulink block diagrams.

Core

Modeling Physical Systems with Simscape

This course focuses on modeling systems in several physical domains and combine them into a multidomain system in the Simulink environment using Simscape.

Modeling Multibody Mechanical Systems with Simscape

This course focuses on how to model rigid-body mechanical systems in the Simulink environment using SimMechanics.

Electrical System Modeling Path

Prerequisite

Simulink for System and Algorithm Modeling

This course is for engineers who are new to system and algorithm modeling and design validation in Simulink. It demonstrates how to apply basic modeling techniques and tools to develop Simulink block diagrams.

Core

Modeling Physical Systems with Simscape

This course focuses on modeling systems in several physical domains and combine them into a multidomain system in the Simulink environment using Simscape.

Modeling Electrical Power Systems with Simscape

This course discusses how to model electrical power systems in the Simulink environment using SimPowerSystems.

Embedded Systems Path

Prerequisite

MATLAB Fundamentals

This course provides a comprehensive introduction to the MATLAB technical computing environment. No prior programming experience or knowledge of MATLAB is assumed. Themes of data analysis, visualization, modeling, and programming are explored throughout the course.

Core

Signal Processing with Simulink

This course, targeted toward new users of Simulink, uses basic modeling techniques and tools to demonstrate how to develop Simulink block diagrams for signal processing applications.

Embedded Coder for Production Code Generation

This hands-on course focuses on developing models in the Simulink environment to deploy on embedded systems. The course is designed for Simulink users who intend to generate, validate, and deploy embedded code using Embedded Coder.

Optional

Programming Xilinx Zynq SoCs with MATLAB and Simulink

This hands-on course focuses on developing and configuring models in the Simulink environment and deploying on Xilinx Zynq-7000 All Programmable SoCs. The course is designed for Simulink users who intend to generate, validate, and deploy embedded code and HDL code for software/hardware codesign using Embedded Coder and HDL Coder. A ZedBoard is provided to each attendee for use throughout the course. The board is programmed during the class and is yours to keep after the training.

FPGA Design Path

Prerequisite

MATLAB Fundamentals

This course provides a comprehensive introduction to the MATLAB technical computing environment. No prior programming experience or knowledge of MATLAB is assumed. Themes of data analysis, visualization, modeling, and programming are explored throughout the course.

Core

Signal Processing with Simulink

This course, targeted toward new users of Simulink, uses basic modeling techniques and tools to demonstrate how to develop Simulink block diagrams for signal processing applications.

Generating HDL Code from Simulink

This course shows how to generate and verify HDL code from a Simulink model using HDL Coder and HDL Verifier.

 

Optional

Programming Xilinx Zynq SoCs with MATLAB and Simulink

This hands-on course focuses on developing and configuring models in the Simulink environment and deploying on Xilinx Zynq-7000 All Programmable SoCs. The course is designed for Simulink users who intend to generate, validate, and deploy embedded code and HDL code for software/hardware codesign using Embedded Coder and HDL Coder. A ZedBoard is provided to each attendee for use throughout the course. The board is programmed during the class and is yours to keep after the training.

Prerequisite

MATLAB Fundamentals

This course provides a comprehensive introduction to the MATLAB technical computing environment. No prior programming experience or knowledge of MATLAB is assumed. Themes of data analysis, visualization, modeling, and programming are explored throughout the course.

Signal Processing with Simulink

This course, targeted toward new users of Simulink, uses basic modeling techniques and tools to demonstrate how to develop Simulink block diagrams for signal processing applications.

Core

Communication Systems Modeling with Simulink

Using hands-on examples, this course demonstrates the use of Simulink products to design common communication systems. The emphasis is on designing end-to-end communication systems using Simulink, Communications System Toolbox, and DSP System Toolbox.

Generating HDL Code from Simulink

This course shows how to generate and verify HDL code from a Simulink model using HDL Coder and HDL Verifier.

Optional

Programming Xilinx Zynq SoCs with MATLAB and Simulink

This hands-on course focuses on developing and configuring models in the Simulink environment and deploying on Xilinx Zynq-7000 All Programmable SoCs. The course is designed for Simulink users who intend to generate, validate, and deploy embedded code and HDL code for software/hardware codesign using Embedded Coder and HDL Coder. A ZedBoard is provided to each attendee for use throughout the course. The board is programmed during the class and is yours to keep after the training.


 Live, online classes led by MathWorks instructors

 Online, self-paced training through MATLAB Academy

 Held in classroom settings throughout the world

 Customized instruction at your work site

Recommended preparation for MATLAB Associate Certification exam

Recommended preparation for MATLAB Professional Certification exam